CAJ | 학술논문

运用动态谐波回归模型(Dynamic Harmonic Regression,DHR)对MODIS的长时间序列的LAI产品进行分析,可以从中分离出LAI随时间变化的多年趋势、季节变化及残差等主要成分,通过建立的模型实现LAI年间变化的短时预测.本文将所述DHR模型分析方法试用于遥感数据产品随时间变化的信息提取,对LAI年间变化的预测结果证明该方法用于遥感像元尺度LAI产晶的时间序列分析与预测的效果良好.
운용동태해파회귀모형(Dynamic Harmonic Regression,DHR)대MODIS적장시간서렬적LAI산품진행분석,가이종중분리출LAI수시간변화적다년추세、계절변화급잔차등주요성분,통과건립적모형실현LAI년간변화적단시예측.본문장소술DHR모형분석방법시용우요감수거산품수시간변화적신식제취,대LAI년간변화적예측결과증명해방법용우요감상원척도LAI산정적시간서렬분석여예측적효과량호.
Leaf Area Index (LAI) is one of the most important parameters in describing the dynamics of vegetation on land surfaces. LAI products have been produced from data of many remote sensing satellite sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS). In this paper, we used the Dynamic Harmonic Regression (DHR) model to analyze the LAI time series products. The model can decompose the trend, seasonal and residuals components from the original time series, and predict the short-time LAI values. We use the DHR model to extract the time change information from the MODIS LAI time series products. The results show this method to be very effective in predicting the short-term LAI on the pixel basis.